Overview

Dataset statistics

Number of variables21
Number of observations114000
Missing cells3
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.5 MiB
Average record size in memory161.0 B

Variable types

Numeric13
Text5
Boolean1
Categorical2

Alerts

energy is highly overall correlated with loudness and 1 other fieldsHigh correlation
loudness is highly overall correlated with energy and 1 other fieldsHigh correlation
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
explicit is highly imbalanced (57.9%)Imbalance
time_signature is highly imbalanced (73.9%)Imbalance
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
popularity has 16020 (14.1%) zerosZeros
key has 13061 (11.5%) zerosZeros
instrumentalness has 38763 (34.0%) zerosZeros

Reproduction

Analysis started2023-11-10 13:10:36.683568
Analysis finished2023-11-10 13:11:23.569367
Duration46.89 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct114000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56999.5
Minimum0
Maximum113999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2023-11-10T14:11:23.749712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5699.95
Q128499.75
median56999.5
Q385499.25
95-th percentile108299.05
Maximum113999
Range113999
Interquartile range (IQR)56999.5

Descriptive statistics

Standard deviation32909.11
Coefficient of variation (CV)0.57735787
Kurtosis-1.2
Mean56999.5
Median Absolute Deviation (MAD)28500
Skewness0
Sum6.497943 × 109
Variance1.0830095 × 109
MonotonicityStrictly increasing
2023-11-10T14:11:24.030893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
75997 1
 
< 0.1%
76008 1
 
< 0.1%
76007 1
 
< 0.1%
76006 1
 
< 0.1%
76005 1
 
< 0.1%
76004 1
 
< 0.1%
76003 1
 
< 0.1%
76002 1
 
< 0.1%
76001 1
 
< 0.1%
Other values (113990) 113990
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
113999 1
< 0.1%
113998 1
< 0.1%
113997 1
< 0.1%
113996 1
< 0.1%
113995 1
< 0.1%
113994 1
< 0.1%
113993 1
< 0.1%
113992 1
< 0.1%
113991 1
< 0.1%
113990 1
< 0.1%
Distinct89741
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Memory size890.8 KiB
2023-11-10T14:11:24.563748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2508000
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73100 ?
Unique (%)64.1%

Sample

1st row5SuOikwiRyPMVoIQDJUgSV
2nd row4qPNDBW1i3p13qLCt0Ki3A
3rd row1iJBSr7s7jYXzM8EGcbK5b
4th row6lfxq3CG4xtTiEg7opyCyx
5th row5vjLSffimiIP26QG5WcN2K
ValueCountFrequency (%)
6s3jldagk3uu3ntzbpnuhs 9
 
< 0.1%
2kkvb3rnrzwjfdghaua0tz 8
 
< 0.1%
2ey6v4sekh3z0rusisrosd 8
 
< 0.1%
4aqs25f3ywj9tgnnkoqilc 7
 
< 0.1%
5sqkarfxe7uejhtlcthcls 7
 
< 0.1%
6bzwr3epseolvwlblk58il 7
 
< 0.1%
54zcdkbialanv8ihi3xwld 7
 
< 0.1%
4xyiegksljlhpzb3bl6wmp 7
 
< 0.1%
5bi1xqmjk91dseq0bfe0ov 7
 
< 0.1%
5ftfvzslii5zxydnbrtf41 7
 
< 0.1%
Other values (89731) 113926
99.9%
2023-11-10T14:11:25.188601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 53778
 
2.1%
5 53497
 
2.1%
2 53335
 
2.1%
6 53275
 
2.1%
0 53232
 
2.1%
1 53162
 
2.1%
4 53152
 
2.1%
7 50535
 
2.0%
K 39217
 
1.6%
D 39104
 
1.6%
Other values (52) 2005713
80.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1003279
40.0%
Uppercase Letter 1003032
40.0%
Decimal Number 501689
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 39217
 
3.9%
D 39104
 
3.9%
G 38991
 
3.9%
A 38963
 
3.9%
W 38756
 
3.9%
E 38755
 
3.9%
X 38726
 
3.9%
B 38719
 
3.9%
I 38705
 
3.9%
R 38684
 
3.9%
Other values (16) 614412
61.3%
Lowercase Letter
ValueCountFrequency (%)
k 39055
 
3.9%
f 38989
 
3.9%
h 38937
 
3.9%
l 38920
 
3.9%
y 38878
 
3.9%
e 38851
 
3.9%
p 38773
 
3.9%
i 38754
 
3.9%
b 38724
 
3.9%
u 38684
 
3.9%
Other values (16) 614714
61.3%
Decimal Number
ValueCountFrequency (%)
3 53778
10.7%
5 53497
10.7%
2 53335
10.6%
6 53275
10.6%
0 53232
10.6%
1 53162
10.6%
4 53152
10.6%
7 50535
10.1%
8 39097
7.8%
9 38626
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 2006311
80.0%
Common 501689
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 39217
 
2.0%
D 39104
 
1.9%
k 39055
 
1.9%
G 38991
 
1.9%
f 38989
 
1.9%
A 38963
 
1.9%
h 38937
 
1.9%
l 38920
 
1.9%
y 38878
 
1.9%
e 38851
 
1.9%
Other values (42) 1616406
80.6%
Common
ValueCountFrequency (%)
3 53778
10.7%
5 53497
10.7%
2 53335
10.6%
6 53275
10.6%
0 53232
10.6%
1 53162
10.6%
4 53152
10.6%
7 50535
10.1%
8 39097
7.8%
9 38626
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2508000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 53778
 
2.1%
5 53497
 
2.1%
2 53335
 
2.1%
6 53275
 
2.1%
0 53232
 
2.1%
1 53162
 
2.1%
4 53152
 
2.1%
7 50535
 
2.0%
K 39217
 
1.6%
D 39104
 
1.6%
Other values (52) 2005713
80.0%
Distinct31437
Distinct (%)27.6%
Missing1
Missing (%)< 0.1%
Memory size890.8 KiB
2023-11-10T14:11:25.740816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length513
Median length322
Mean length16.319354
Min length2

Characters and Unicode

Total characters1860390
Distinct characters712
Distinct categories18 ?
Distinct scripts7 ?
Distinct blocks14 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16767 ?
Unique (%)14.7%

Sample

1st rowGen Hoshino
2nd rowBen Woodward
3rd rowIngrid Michaelson;ZAYN
4th rowKina Grannis
5th rowChord Overstreet
ValueCountFrequency (%)
the 6831
 
2.6%
3126
 
1.2%
de 1133
 
0.4%
los 1066
 
0.4%
of 1034
 
0.4%
dj 738
 
0.3%
george 593
 
0.2%
jones 524
 
0.2%
la 518
 
0.2%
for 457
 
0.2%
Other values (42276) 241844
93.8%
2023-11-10T14:11:26.502355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 164229
 
8.8%
e 148733
 
8.0%
143873
 
7.7%
i 112151
 
6.0%
n 106549
 
5.7%
o 103832
 
5.6%
r 100226
 
5.4%
l 75690
 
4.1%
s 69313
 
3.7%
t 63612
 
3.4%
Other values (702) 772182
41.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1313318
70.6%
Uppercase Letter 338499
 
18.2%
Space Separator 143873
 
7.7%
Other Punctuation 53924
 
2.9%
Decimal Number 5642
 
0.3%
Dash Punctuation 2092
 
0.1%
Other Letter 2008
 
0.1%
Currency Symbol 289
 
< 0.1%
Close Punctuation 181
 
< 0.1%
Open Punctuation 179
 
< 0.1%
Other values (8) 385
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
63
 
3.1%
59
 
2.9%
56
 
2.8%
49
 
2.4%
43
 
2.1%
42
 
2.1%
41
 
2.0%
41
 
2.0%
33
 
1.6%
26
 
1.3%
Other values (453) 1555
77.4%
Lowercase Letter
ValueCountFrequency (%)
a 164229
12.5%
e 148733
11.3%
i 112151
 
8.5%
n 106549
 
8.1%
o 103832
 
7.9%
r 100226
 
7.6%
l 75690
 
5.8%
s 69313
 
5.3%
t 63612
 
4.8%
h 51170
 
3.9%
Other values (102) 317813
24.2%
Uppercase Letter
ValueCountFrequency (%)
S 29141
 
8.6%
A 23980
 
7.1%
M 23977
 
7.1%
B 22899
 
6.8%
T 20678
 
6.1%
C 20253
 
6.0%
D 18520
 
5.5%
R 17533
 
5.2%
L 16695
 
4.9%
P 15667
 
4.6%
Other values (66) 129156
38.2%
Other Punctuation
ValueCountFrequency (%)
; 44293
82.1%
. 3781
 
7.0%
& 2993
 
5.6%
' 1313
 
2.4%
" 566
 
1.0%
! 307
 
0.6%
, 286
 
0.5%
/ 162
 
0.3%
: 155
 
0.3%
? 32
 
0.1%
Other values (9) 36
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 887
15.7%
2 817
14.5%
4 704
12.5%
3 684
12.1%
0 536
9.5%
7 452
8.0%
8 440
7.8%
6 416
7.4%
9 381
6.8%
5 325
 
5.8%
Close Punctuation
ValueCountFrequency (%)
) 147
81.2%
] 24
 
13.3%
5
 
2.8%
3
 
1.7%
} 2
 
1.1%
Math Symbol
ValueCountFrequency (%)
+ 82
52.9%
= 48
31.0%
23
 
14.8%
| 1
 
0.6%
1
 
0.6%
Other Symbol
ValueCountFrequency (%)
6
37.5%
5
31.2%
2
 
12.5%
® 2
 
12.5%
1
 
6.2%
Open Punctuation
ValueCountFrequency (%)
( 147
82.1%
[ 24
 
13.4%
5
 
2.8%
3
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 1999
95.6%
93
 
4.4%
Modifier Letter
ValueCountFrequency (%)
117
98.3%
2
 
1.7%
Final Punctuation
ValueCountFrequency (%)
49
98.0%
1
 
2.0%
Initial Punctuation
ValueCountFrequency (%)
4
80.0%
1
 
20.0%
Space Separator
ValueCountFrequency (%)
143873
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 289
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 20
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 11
100.0%
Other Number
ValueCountFrequency (%)
² 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1645353
88.4%
Common 206570
 
11.1%
Cyrillic 6454
 
0.3%
Han 1291
 
0.1%
Katakana 622
 
< 0.1%
Hiragana 97
 
< 0.1%
Greek 3
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
33
 
2.6%
26
 
2.0%
23
 
1.8%
20
 
1.5%
19
 
1.5%
19
 
1.5%
18
 
1.4%
18
 
1.4%
18
 
1.4%
17
 
1.3%
Other values (380) 1080
83.7%
Latin
ValueCountFrequency (%)
a 164229
 
10.0%
e 148733
 
9.0%
i 112151
 
6.8%
n 106549
 
6.5%
o 103832
 
6.3%
r 100226
 
6.1%
l 75690
 
4.6%
s 69313
 
4.2%
t 63612
 
3.9%
h 51170
 
3.1%
Other values (120) 649848
39.5%
Common
ValueCountFrequency (%)
143873
69.6%
; 44293
 
21.4%
. 3781
 
1.8%
& 2993
 
1.4%
- 1999
 
1.0%
' 1313
 
0.6%
1 887
 
0.4%
2 817
 
0.4%
4 704
 
0.3%
3 684
 
0.3%
Other values (51) 5226
 
2.5%
Cyrillic
ValueCountFrequency (%)
а 816
 
12.6%
о 479
 
7.4%
р 457
 
7.1%
и 429
 
6.6%
е 393
 
6.1%
н 387
 
6.0%
к 279
 
4.3%
в 274
 
4.2%
л 255
 
4.0%
с 227
 
3.5%
Other values (45) 2458
38.1%
Katakana
ValueCountFrequency (%)
63
 
10.1%
59
 
9.5%
56
 
9.0%
49
 
7.9%
43
 
6.9%
42
 
6.8%
41
 
6.6%
41
 
6.6%
19
 
3.1%
18
 
2.9%
Other values (35) 191
30.7%
Hiragana
ValueCountFrequency (%)
10
 
10.3%
8
 
8.2%
7
 
7.2%
6
 
6.2%
6
 
6.2%
6
 
6.2%
6
 
6.2%
5
 
5.2%
5
 
5.2%
4
 
4.1%
Other values (19) 34
35.1%
Greek
ValueCountFrequency (%)
α 2
66.7%
μ 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1842231
99.0%
Latin 1 Sup 8556
 
0.5%
Cyrillic 6454
 
0.3%
CJK 1289
 
0.1%
Latin Ext A 806
 
< 0.1%
Katakana 740
 
< 0.1%
Punctuation 149
 
< 0.1%
Hiragana 97
 
< 0.1%
None 25
 
< 0.1%
Math Operators 24
 
< 0.1%
Other values (4) 19
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 164229
 
8.9%
e 148733
 
8.1%
143873
 
7.8%
i 112151
 
6.1%
n 106549
 
5.8%
o 103832
 
5.6%
r 100226
 
5.4%
l 75690
 
4.1%
s 69313
 
3.8%
t 63612
 
3.5%
Other values (78) 754023
40.9%
Latin 1 Sup
ValueCountFrequency (%)
é 1493
17.4%
ã 885
10.3%
á 723
 
8.5%
ö 697
 
8.1%
ó 589
 
6.9%
í 570
 
6.7%
ü 502
 
5.9%
ä 419
 
4.9%
ç 362
 
4.2%
ë 296
 
3.5%
Other values (42) 2020
23.6%
Cyrillic
ValueCountFrequency (%)
а 816
 
12.6%
о 479
 
7.4%
р 457
 
7.1%
и 429
 
6.6%
е 393
 
6.1%
н 387
 
6.0%
к 279
 
4.3%
в 274
 
4.2%
л 255
 
4.0%
с 227
 
3.5%
Other values (45) 2458
38.1%
Latin Ext A
ValueCountFrequency (%)
ı 236
29.3%
ş 176
21.8%
ğ 88
 
10.9%
Ş 87
 
10.8%
İ 58
 
7.2%
ł 38
 
4.7%
ř 19
 
2.4%
č 18
 
2.2%
š 12
 
1.5%
ũ 10
 
1.2%
Other values (19) 64
 
7.9%
Katakana
ValueCountFrequency (%)
117
15.8%
63
 
8.5%
59
 
8.0%
56
 
7.6%
49
 
6.6%
43
 
5.8%
42
 
5.7%
41
 
5.5%
41
 
5.5%
19
 
2.6%
Other values (37) 210
28.4%
Punctuation
ValueCountFrequency (%)
93
62.4%
49
32.9%
4
 
2.7%
1
 
0.7%
1
 
0.7%
1
 
0.7%
CJK
ValueCountFrequency (%)
33
 
2.6%
26
 
2.0%
23
 
1.8%
20
 
1.6%
19
 
1.5%
19
 
1.5%
18
 
1.4%
18
 
1.4%
18
 
1.4%
17
 
1.3%
Other values (379) 1078
83.6%
Math Operators
ValueCountFrequency (%)
23
95.8%
1
 
4.2%
Hiragana
ValueCountFrequency (%)
10
 
10.3%
8
 
8.2%
7
 
7.2%
6
 
6.2%
6
 
6.2%
6
 
6.2%
6
 
6.2%
5
 
5.2%
5
 
5.2%
4
 
4.1%
Other values (19) 34
35.1%
Misc Symbols
ValueCountFrequency (%)
6
100.0%
None
ValueCountFrequency (%)
5
20.0%
5
20.0%
3
12.0%
3
12.0%
3
12.0%
2
 
8.0%
α 2
 
8.0%
1
 
4.0%
μ 1
 
4.0%
Letterlike Symbols
ValueCountFrequency (%)
5
100.0%
Latin Ext Additional
ValueCountFrequency (%)
3
60.0%
2
40.0%
Dingbats
ValueCountFrequency (%)
2
66.7%
1
33.3%
Distinct46589
Distinct (%)40.9%
Missing1
Missing (%)< 0.1%
Memory size890.8 KiB
2023-11-10T14:11:27.015040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length243
Median length145
Mean length20.116668
Min length1

Characters and Unicode

Total characters2293280
Distinct characters2084
Distinct categories22 ?
Distinct scripts13 ?
Distinct blocks24 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27955 ?
Unique (%)24.5%

Sample

1st rowComedy
2nd rowGhost (Acoustic)
3rd rowTo Begin Again
4th rowCrazy Rich Asians (Original Motion Picture Soundtrack)
5th rowHold On
ValueCountFrequency (%)
the 12029
 
3.1%
9198
 
2.3%
of 5240
 
1.3%
2022 3430
 
0.9%
vol 3257
 
0.8%
christmas 3214
 
0.8%
vivo 3186
 
0.8%
a 3174
 
0.8%
ao 2929
 
0.7%
de 2893
 
0.7%
Other values (35981) 343235
87.6%
2023-11-10T14:11:27.738350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
277786
 
12.1%
e 184978
 
8.1%
a 142803
 
6.2%
o 138424
 
6.0%
i 127748
 
5.6%
n 106159
 
4.6%
r 105849
 
4.6%
s 96731
 
4.2%
t 96378
 
4.2%
l 79067
 
3.4%
Other values (2074) 937357
40.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1499424
65.4%
Uppercase Letter 364971
 
15.9%
Space Separator 277786
 
12.1%
Decimal Number 50359
 
2.2%
Other Punctuation 32819
 
1.4%
Other Letter 22050
 
1.0%
Close Punctuation 18361
 
0.8%
Open Punctuation 18359
 
0.8%
Dash Punctuation 7237
 
0.3%
Math Symbol 833
 
< 0.1%
Other values (12) 1081
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
399
 
1.8%
375
 
1.7%
291
 
1.3%
287
 
1.3%
255
 
1.2%
235
 
1.1%
223
 
1.0%
210
 
1.0%
193
 
0.9%
179
 
0.8%
Other values (1728) 19403
88.0%
Lowercase Letter
ValueCountFrequency (%)
e 184978
12.3%
a 142803
 
9.5%
o 138424
 
9.2%
i 127748
 
8.5%
n 106159
 
7.1%
r 105849
 
7.1%
s 96731
 
6.5%
t 96378
 
6.4%
l 79067
 
5.3%
u 52306
 
3.5%
Other values (128) 368981
24.6%
Uppercase Letter
ValueCountFrequency (%)
S 30218
 
8.3%
T 27866
 
7.6%
A 26171
 
7.2%
M 22698
 
6.2%
C 22365
 
6.1%
P 19741
 
5.4%
R 19064
 
5.2%
B 18069
 
5.0%
E 18023
 
4.9%
D 17769
 
4.9%
Other values (91) 142987
39.2%
Other Punctuation
ValueCountFrequency (%)
. 9198
28.0%
' 5195
15.8%
, 4878
14.9%
: 4462
13.6%
& 2559
 
7.8%
/ 2109
 
6.4%
" 1606
 
4.9%
! 1249
 
3.8%
? 500
 
1.5%
# 247
 
0.8%
Other values (13) 816
 
2.5%
Nonspacing Mark
ValueCountFrequency (%)
57
51.4%
̆ 13
 
11.7%
́ 12
 
10.8%
8
 
7.2%
̈ 6
 
5.4%
4
 
3.6%
3
 
2.7%
̀ 2
 
1.8%
2
 
1.8%
1
 
0.9%
Other values (3) 3
 
2.7%
Decimal Number
ValueCountFrequency (%)
2 18813
37.4%
0 12475
24.8%
1 6993
 
13.9%
9 2621
 
5.2%
3 2418
 
4.8%
5 1894
 
3.8%
4 1473
 
2.9%
7 1297
 
2.6%
6 1213
 
2.4%
8 1162
 
2.3%
Math Symbol
ValueCountFrequency (%)
+ 301
36.1%
~ 271
32.5%
| 86
 
10.3%
> 69
 
8.3%
< 66
 
7.9%
= 20
 
2.4%
10
 
1.2%
× 7
 
0.8%
÷ 2
 
0.2%
1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 17382
94.7%
[ 810
 
4.4%
54
 
0.3%
51
 
0.3%
43
 
0.2%
10
 
0.1%
{ 8
 
< 0.1%
1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 17381
94.7%
] 813
 
4.4%
54
 
0.3%
51
 
0.3%
43
 
0.2%
10
 
0.1%
} 8
 
< 0.1%
1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
® 20
32.8%
° 17
27.9%
14
23.0%
4
 
6.6%
2
 
3.3%
2
 
3.3%
1
 
1.6%
1
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 6981
96.5%
185
 
2.6%
60
 
0.8%
6
 
0.1%
4
 
0.1%
1
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 30
90.9%
` 2
 
6.1%
˚ 1
 
3.0%
Letter Number
ValueCountFrequency (%)
15
75.0%
4
 
20.0%
1
 
5.0%
Spacing Mark
ValueCountFrequency (%)
5
55.6%
3
33.3%
1
 
11.1%
Modifier Letter
ValueCountFrequency (%)
396
95.2%
20
 
4.8%
Final Punctuation
ValueCountFrequency (%)
205
81.7%
46
 
18.3%
Initial Punctuation
ValueCountFrequency (%)
54
90.0%
6
 
10.0%
Format
ValueCountFrequency (%)
14
87.5%
2
 
12.5%
Space Separator
ValueCountFrequency (%)
277786
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 72
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 31
100.0%
Other Number
ValueCountFrequency (%)
² 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1845383
80.5%
Common 406675
 
17.7%
Cyrillic 18965
 
0.8%
Han 13340
 
0.6%
Katakana 5077
 
0.2%
Hiragana 3518
 
0.2%
Inherited 102
 
< 0.1%
Greek 80
 
< 0.1%
Hangul 47
 
< 0.1%
Arabic 44
 
< 0.1%
Other values (3) 49
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
291
 
2.2%
235
 
1.8%
193
 
1.4%
134
 
1.0%
134
 
1.0%
132
 
1.0%
124
 
0.9%
117
 
0.9%
111
 
0.8%
109
 
0.8%
Other values (1537) 11760
88.2%
Latin
ValueCountFrequency (%)
e 184978
 
10.0%
a 142803
 
7.7%
o 138424
 
7.5%
i 127748
 
6.9%
n 106159
 
5.8%
r 105849
 
5.7%
s 96731
 
5.2%
t 96378
 
5.2%
l 79067
 
4.3%
u 52306
 
2.8%
Other values (144) 714940
38.7%
Common
ValueCountFrequency (%)
277786
68.3%
2 18813
 
4.6%
( 17382
 
4.3%
) 17381
 
4.3%
0 12475
 
3.1%
. 9198
 
2.3%
1 6993
 
1.7%
- 6981
 
1.7%
' 5195
 
1.3%
, 4878
 
1.2%
Other values (77) 29593
 
7.3%
Katakana
ValueCountFrequency (%)
399
 
7.9%
287
 
5.7%
255
 
5.0%
210
 
4.1%
179
 
3.5%
169
 
3.3%
166
 
3.3%
160
 
3.2%
151
 
3.0%
145
 
2.9%
Other values (68) 2956
58.2%
Hiragana
ValueCountFrequency (%)
375
 
10.7%
223
 
6.3%
176
 
5.0%
155
 
4.4%
143
 
4.1%
134
 
3.8%
127
 
3.6%
119
 
3.4%
110
 
3.1%
106
 
3.0%
Other values (60) 1850
52.6%
Cyrillic
ValueCountFrequency (%)
а 1613
 
8.5%
е 1611
 
8.5%
о 1439
 
7.6%
н 1314
 
6.9%
с 1289
 
6.8%
и 1226
 
6.5%
р 896
 
4.7%
т 894
 
4.7%
к 749
 
3.9%
л 727
 
3.8%
Other values (53) 7207
38.0%
Greek
ValueCountFrequency (%)
φ 27
33.8%
α 6
 
7.5%
Ξ 5
 
6.2%
ς 4
 
5.0%
μ 3
 
3.8%
Ψ 3
 
3.8%
ε 2
 
2.5%
ή 2
 
2.5%
τ 2
 
2.5%
ό 2
 
2.5%
Other values (16) 24
30.0%
Malayalam
ValueCountFrequency (%)
5
13.2%
4
10.5%
4
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
Other values (8) 8
21.1%
Hangul
ValueCountFrequency (%)
8
17.0%
6
12.8%
4
8.5%
4
8.5%
4
8.5%
4
8.5%
4
8.5%
2
 
4.3%
2
 
4.3%
2
 
4.3%
Other values (6) 7
14.9%
Arabic
ValueCountFrequency (%)
ا 8
18.2%
ی 6
13.6%
چ 4
9.1%
ه 4
9.1%
و 4
9.1%
ت 4
9.1%
ر 4
9.1%
ک 4
9.1%
ل 4
9.1%
م 2
 
4.5%
Inherited
ValueCountFrequency (%)
57
55.9%
̆ 13
 
12.7%
́ 12
 
11.8%
8
 
7.8%
̈ 6
 
5.9%
̀ 2
 
2.0%
2
 
2.0%
̊ 1
 
1.0%
̃ 1
 
1.0%
Thai
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Devanagari
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2236463
97.5%
Cyrillic 18965
 
0.8%
CJK 13319
 
0.6%
Latin 1 Sup 12203
 
0.5%
Katakana 5702
 
0.2%
Hiragana 3583
 
0.2%
Latin Ext A 1568
 
0.1%
None 747
 
< 0.1%
Punctuation 455
 
< 0.1%
Hangul 47
 
< 0.1%
Other values (14) 228
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
277786
 
12.4%
e 184978
 
8.3%
a 142803
 
6.4%
o 138424
 
6.2%
i 127748
 
5.7%
n 106159
 
4.7%
r 105849
 
4.7%
s 96731
 
4.3%
t 96378
 
4.3%
l 79067
 
3.5%
Other values (83) 880540
39.4%
Cyrillic
ValueCountFrequency (%)
а 1613
 
8.5%
е 1611
 
8.5%
о 1439
 
7.6%
н 1314
 
6.9%
с 1289
 
6.8%
и 1226
 
6.5%
р 896
 
4.7%
т 894
 
4.7%
к 749
 
3.9%
л 727
 
3.8%
Other values (53) 7207
38.0%
Latin 1 Sup
ValueCountFrequency (%)
ó 1193
 
9.8%
ã 1163
 
9.5%
á 1148
 
9.4%
é 1070
 
8.8%
ç 845
 
6.9%
ú 836
 
6.9%
ñ 745
 
6.1%
ü 728
 
6.0%
í 718
 
5.9%
ä 620
 
5.1%
Other values (57) 3137
25.7%
Latin Ext A
ValueCountFrequency (%)
ı 672
42.9%
ş 239
 
15.2%
İ 132
 
8.4%
ğ 111
 
7.1%
Ş 106
 
6.8%
ę 45
 
2.9%
ł 34
 
2.2%
ż 28
 
1.8%
ś 22
 
1.4%
Ś 22
 
1.4%
Other values (24) 157
 
10.0%
Katakana
ValueCountFrequency (%)
399
 
7.0%
396
 
6.9%
287
 
5.0%
255
 
4.5%
229
 
4.0%
210
 
3.7%
179
 
3.1%
169
 
3.0%
166
 
2.9%
160
 
2.8%
Other values (70) 3252
57.0%
Hiragana
ValueCountFrequency (%)
375
 
10.5%
223
 
6.2%
176
 
4.9%
155
 
4.3%
143
 
4.0%
134
 
3.7%
127
 
3.5%
119
 
3.3%
110
 
3.1%
106
 
3.0%
Other values (62) 1915
53.4%
CJK
ValueCountFrequency (%)
291
 
2.2%
235
 
1.8%
193
 
1.4%
134
 
1.0%
134
 
1.0%
132
 
1.0%
124
 
0.9%
117
 
0.9%
111
 
0.8%
109
 
0.8%
Other values (1535) 11739
88.1%
Punctuation
ValueCountFrequency (%)
205
45.1%
60
 
13.2%
54
 
11.9%
48
 
10.5%
46
 
10.1%
14
 
3.1%
6
 
1.3%
6
 
1.3%
6
 
1.3%
4
 
0.9%
Other values (3) 6
 
1.3%
None
ValueCountFrequency (%)
185
24.8%
97
13.0%
54
 
7.2%
54
 
7.2%
51
 
6.8%
51
 
6.8%
46
 
6.2%
43
 
5.8%
43
 
5.8%
φ 27
 
3.6%
Other values (31) 96
12.9%
IPA Ext
ValueCountFrequency (%)
ə 20
100.0%
Number Forms
ValueCountFrequency (%)
15
78.9%
4
 
21.1%
Misc Symbols
ValueCountFrequency (%)
14
70.0%
4
 
20.0%
2
 
10.0%
Diacriticals
ValueCountFrequency (%)
̆ 13
37.1%
́ 12
34.3%
̈ 6
17.1%
̀ 2
 
5.7%
̊ 1
 
2.9%
̃ 1
 
2.9%
Math Operators
ValueCountFrequency (%)
10
90.9%
1
 
9.1%
Hangul
ValueCountFrequency (%)
8
17.0%
6
12.8%
4
8.5%
4
8.5%
4
8.5%
4
8.5%
4
8.5%
2
 
4.3%
2
 
4.3%
2
 
4.3%
Other values (6) 7
14.9%
Arabic
ValueCountFrequency (%)
ا 8
18.2%
ی 6
13.6%
چ 4
9.1%
ه 4
9.1%
و 4
9.1%
ت 4
9.1%
ر 4
9.1%
ک 4
9.1%
ل 4
9.1%
م 2
 
4.5%
Devanagari
ValueCountFrequency (%)
6
100.0%
Latin Ext Additional
ValueCountFrequency (%)
6
26.1%
6
26.1%
3
13.0%
2
 
8.7%
2
 
8.7%
2
 
8.7%
2
 
8.7%
Malayalam
ValueCountFrequency (%)
5
13.2%
4
10.5%
4
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
Other values (8) 8
21.1%
Dingbats
ValueCountFrequency (%)
2
66.7%
1
33.3%
VS
ValueCountFrequency (%)
2
100.0%
Geometric Shapes
ValueCountFrequency (%)
1
100.0%
Thai
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Modifier Letters
ValueCountFrequency (%)
˚ 1
100.0%
Distinct73608
Distinct (%)64.6%
Missing1
Missing (%)< 0.1%
Memory size890.8 KiB
2023-11-10T14:11:28.226063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length511
Median length146
Mean length17.994684
Min length1

Characters and Unicode

Total characters2051376
Distinct characters2417
Distinct categories23 ?
Distinct scripts13 ?
Distinct blocks29 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55711 ?
Unique (%)48.9%

Sample

1st rowComedy
2nd rowGhost - Acoustic
3rd rowTo Begin Again
4th rowCan't Help Falling In Love
5th rowHold On
ValueCountFrequency (%)
19654
 
5.1%
the 9471
 
2.5%
you 4292
 
1.1%
me 3716
 
1.0%
a 3696
 
1.0%
of 3605
 
0.9%
i 3409
 
0.9%
in 3180
 
0.8%
vivo 3158
 
0.8%
remix 2984
 
0.8%
Other values (50550) 328614
85.2%
2023-11-10T14:11:28.940818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
271780
 
13.2%
e 174853
 
8.5%
a 136251
 
6.6%
o 122444
 
6.0%
i 109433
 
5.3%
n 94021
 
4.6%
r 92079
 
4.5%
t 81895
 
4.0%
s 67733
 
3.3%
l 63149
 
3.1%
Other values (2407) 837738
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1322573
64.5%
Uppercase Letter 342166
 
16.7%
Space Separator 271780
 
13.2%
Other Punctuation 30401
 
1.5%
Other Letter 23202
 
1.1%
Decimal Number 21475
 
1.0%
Dash Punctuation 17861
 
0.9%
Open Punctuation 10046
 
0.5%
Close Punctuation 10043
 
0.5%
Modifier Letter 616
 
< 0.1%
Other values (13) 1213
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
486
 
2.1%
431
 
1.9%
344
 
1.5%
305
 
1.3%
282
 
1.2%
236
 
1.0%
230
 
1.0%
219
 
0.9%
217
 
0.9%
216
 
0.9%
Other values (2056) 20236
87.2%
Lowercase Letter
ValueCountFrequency (%)
e 174853
13.2%
a 136251
10.3%
o 122444
 
9.3%
i 109433
 
8.3%
n 94021
 
7.1%
r 92079
 
7.0%
t 81895
 
6.2%
s 67733
 
5.1%
l 63149
 
4.8%
u 47642
 
3.6%
Other values (135) 333073
25.2%
Uppercase Letter
ValueCountFrequency (%)
S 27792
 
8.1%
T 25354
 
7.4%
M 24538
 
7.2%
A 24257
 
7.1%
L 18809
 
5.5%
C 18140
 
5.3%
R 17783
 
5.2%
D 17415
 
5.1%
B 16803
 
4.9%
I 15197
 
4.4%
Other values (84) 136078
39.8%
Other Punctuation
ValueCountFrequency (%)
. 7423
24.4%
' 6630
21.8%
, 4558
15.0%
" 3525
11.6%
/ 2376
 
7.8%
: 1968
 
6.5%
& 1478
 
4.9%
! 1110
 
3.7%
? 780
 
2.6%
169
 
0.6%
Other values (11) 384
 
1.3%
Nonspacing Mark
ValueCountFrequency (%)
́ 50
36.0%
26
18.7%
̃ 14
 
10.1%
̧ 12
 
8.6%
̈ 9
 
6.5%
̂ 7
 
5.0%
5
 
3.6%
̆ 5
 
3.6%
̊ 3
 
2.2%
2
 
1.4%
Other values (5) 6
 
4.3%
Decimal Number
ValueCountFrequency (%)
0 4889
22.8%
2 4797
22.3%
1 3921
18.3%
9 2029
9.4%
3 1105
 
5.1%
4 1105
 
5.1%
5 1068
 
5.0%
8 887
 
4.1%
7 859
 
4.0%
6 815
 
3.8%
Math Symbol
ValueCountFrequency (%)
+ 90
36.6%
~ 52
21.1%
| 51
20.7%
= 20
 
8.1%
> 16
 
6.5%
< 12
 
4.9%
2
 
0.8%
1
 
0.4%
1
 
0.4%
× 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 9568
95.2%
[ 358
 
3.6%
77
 
0.8%
35
 
0.3%
3
 
< 0.1%
2
 
< 0.1%
{ 1
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
11
28.9%
° 11
28.9%
8
21.1%
® 2
 
5.3%
2
 
5.3%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
Close Punctuation
ValueCountFrequency (%)
) 9567
95.3%
] 357
 
3.6%
77
 
0.8%
35
 
0.3%
3
 
< 0.1%
2
 
< 0.1%
} 1
 
< 0.1%
1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 17711
99.2%
112
 
0.6%
26
 
0.1%
10
 
0.1%
2
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 62
80.5%
` 13
 
16.9%
˙ 1
 
1.3%
^ 1
 
1.3%
Final Punctuation
ValueCountFrequency (%)
415
88.1%
46
 
9.8%
» 10
 
2.1%
Initial Punctuation
ValueCountFrequency (%)
76
55.1%
52
37.7%
« 10
 
7.2%
Format
ValueCountFrequency (%)
8
80.0%
1
 
10.0%
 1
 
10.0%
Modifier Letter
ValueCountFrequency (%)
603
97.9%
13
 
2.1%
Currency Symbol
ValueCountFrequency (%)
$ 56
98.2%
1
 
1.8%
Other Number
ValueCountFrequency (%)
½ 2
66.7%
² 1
33.3%
Letter Number
ValueCountFrequency (%)
2
66.7%
1
33.3%
Space Separator
ValueCountFrequency (%)
271780
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 29
100.0%
Private Use
ValueCountFrequency (%)
1
100.0%
Control
ValueCountFrequency (%)
‚ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1652883
80.6%
Common 363278
 
17.7%
Han 13773
 
0.7%
Cyrillic 11823
 
0.6%
Katakana 5060
 
0.2%
Hiragana 4279
 
0.2%
Inherited 138
 
< 0.1%
Hangul 66
 
< 0.1%
Greek 42
 
< 0.1%
Arabic 26
 
< 0.1%
Other values (3) 8
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
344
 
2.5%
236
 
1.7%
230
 
1.7%
207
 
1.5%
204
 
1.5%
201
 
1.5%
158
 
1.1%
131
 
1.0%
122
 
0.9%
115
 
0.8%
Other values (1836) 11825
85.9%
Latin
ValueCountFrequency (%)
e 174853
 
10.6%
a 136251
 
8.2%
o 122444
 
7.4%
i 109433
 
6.6%
n 94021
 
5.7%
r 92079
 
5.6%
t 81895
 
5.0%
s 67733
 
4.1%
l 63149
 
3.8%
u 47642
 
2.9%
Other values (144) 663383
40.1%
Common
ValueCountFrequency (%)
271780
74.8%
- 17711
 
4.9%
( 9568
 
2.6%
) 9567
 
2.6%
. 7423
 
2.0%
' 6630
 
1.8%
0 4889
 
1.3%
2 4797
 
1.3%
, 4558
 
1.3%
1 3921
 
1.1%
Other values (82) 22434
 
6.2%
Katakana
ValueCountFrequency (%)
431
 
8.5%
305
 
6.0%
219
 
4.3%
216
 
4.3%
204
 
4.0%
165
 
3.3%
147
 
2.9%
141
 
2.8%
133
 
2.6%
132
 
2.6%
Other values (69) 2967
58.6%
Hiragana
ValueCountFrequency (%)
486
 
11.4%
282
 
6.6%
217
 
5.1%
165
 
3.9%
135
 
3.2%
132
 
3.1%
129
 
3.0%
122
 
2.9%
118
 
2.8%
111
 
2.6%
Other values (63) 2382
55.7%
Cyrillic
ValueCountFrequency (%)
а 1031
 
8.7%
о 988
 
8.4%
е 903
 
7.6%
и 703
 
5.9%
н 676
 
5.7%
р 584
 
4.9%
т 536
 
4.5%
л 521
 
4.4%
с 495
 
4.2%
к 419
 
3.5%
Other values (53) 4967
42.0%
Hangul
ValueCountFrequency (%)
3
 
4.5%
3
 
4.5%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (38) 43
65.2%
Greek
ValueCountFrequency (%)
ο 4
 
9.5%
τ 3
 
7.1%
ι 3
 
7.1%
Ψ 3
 
7.1%
Σ 3
 
7.1%
Ξ 3
 
7.1%
Χ 3
 
7.1%
ε 2
 
4.8%
φ 2
 
4.8%
Δ 1
 
2.4%
Other values (15) 15
35.7%
Arabic
ValueCountFrequency (%)
د 4
15.4%
م 3
11.5%
ر 3
11.5%
ع 2
 
7.7%
ن 2
 
7.7%
ا 2
 
7.7%
ه 1
 
3.8%
ق 1
 
3.8%
ب 1
 
3.8%
س 1
 
3.8%
Other values (6) 6
23.1%
Inherited
ValueCountFrequency (%)
́ 50
36.2%
26
18.8%
̃ 14
 
10.1%
̧ 12
 
8.7%
̈ 9
 
6.5%
̂ 7
 
5.1%
5
 
3.6%
̆ 5
 
3.6%
̊ 3
 
2.2%
2
 
1.4%
Other values (4) 5
 
3.6%
Thai
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Devanagari
ValueCountFrequency (%)
2
100.0%
Unknown
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000174
97.5%
CJK 13759
 
0.7%
Latin 1 Sup 12608
 
0.6%
Cyrillic 11823
 
0.6%
Katakana 5832
 
0.3%
Hiragana 4310
 
0.2%
Latin Ext A 1431
 
0.1%
Punctuation 676
 
< 0.1%
None 482
 
< 0.1%
Diacriticals 104
 
< 0.1%
Other values (19) 177
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
271780
 
13.6%
e 174853
 
8.7%
a 136251
 
6.8%
o 122444
 
6.1%
i 109433
 
5.5%
n 94021
 
4.7%
r 92079
 
4.6%
t 81895
 
4.1%
s 67733
 
3.4%
l 63149
 
3.2%
Other values (84) 786536
39.3%
Latin 1 Sup
ValueCountFrequency (%)
é 1544
12.2%
ã 1400
11.1%
á 1111
 
8.8%
ó 1100
 
8.7%
í 896
 
7.1%
ç 843
 
6.7%
ä 663
 
5.3%
ê 604
 
4.8%
ü 577
 
4.6%
ú 528
 
4.2%
Other values (55) 3342
26.5%
Cyrillic
ValueCountFrequency (%)
а 1031
 
8.7%
о 988
 
8.4%
е 903
 
7.6%
и 703
 
5.9%
н 676
 
5.7%
р 584
 
4.9%
т 536
 
4.5%
л 521
 
4.4%
с 495
 
4.2%
к 419
 
3.5%
Other values (53) 4967
42.0%
Katakana
ValueCountFrequency (%)
603
 
10.3%
431
 
7.4%
305
 
5.2%
219
 
3.8%
216
 
3.7%
204
 
3.5%
169
 
2.9%
165
 
2.8%
147
 
2.5%
141
 
2.4%
Other values (71) 3232
55.4%
Latin Ext A
ValueCountFrequency (%)
ı 588
41.1%
ş 203
 
14.2%
İ 116
 
8.1%
ğ 109
 
7.6%
Ş 72
 
5.0%
ł 55
 
3.8%
ę 43
 
3.0%
ś 26
 
1.8%
ą 23
 
1.6%
ě 23
 
1.6%
Other values (25) 173
 
12.1%
Hiragana
ValueCountFrequency (%)
486
 
11.3%
282
 
6.5%
217
 
5.0%
165
 
3.8%
135
 
3.1%
132
 
3.1%
129
 
3.0%
122
 
2.8%
118
 
2.7%
111
 
2.6%
Other values (65) 2413
56.0%
Punctuation
ValueCountFrequency (%)
415
61.4%
76
 
11.2%
52
 
7.7%
46
 
6.8%
39
 
5.8%
26
 
3.8%
10
 
1.5%
8
 
1.2%
2
 
0.3%
1
 
0.1%
CJK
ValueCountFrequency (%)
344
 
2.5%
236
 
1.7%
230
 
1.7%
207
 
1.5%
204
 
1.5%
201
 
1.5%
158
 
1.1%
131
 
1.0%
122
 
0.9%
115
 
0.8%
Other values (1834) 11811
85.8%
None
ValueCountFrequency (%)
112
23.2%
77
16.0%
77
16.0%
58
12.0%
35
 
7.3%
35
 
7.3%
20
 
4.1%
13
 
2.7%
ο 4
 
0.8%
τ 3
 
0.6%
Other values (30) 48
10.0%
Diacriticals
ValueCountFrequency (%)
́ 50
48.1%
̃ 14
 
13.5%
̧ 12
 
11.5%
̈ 9
 
8.7%
̂ 7
 
6.7%
̆ 5
 
4.8%
̊ 3
 
2.9%
̀ 2
 
1.9%
̌ 1
 
1.0%
̇ 1
 
1.0%
Misc Symbols
ValueCountFrequency (%)
11
55.0%
8
40.0%
1
 
5.0%
IPA Ext
ValueCountFrequency (%)
ə 10
100.0%
Latin Ext B
ValueCountFrequency (%)
ǎ 6
33.3%
ǐ 5
27.8%
ơ 2
 
11.1%
ư 2
 
11.1%
ǔ 2
 
11.1%
ǒ 1
 
5.6%
Arabic
ValueCountFrequency (%)
د 4
15.4%
م 3
11.5%
ر 3
11.5%
ع 2
 
7.7%
ن 2
 
7.7%
ا 2
 
7.7%
ه 1
 
3.8%
ق 1
 
3.8%
ب 1
 
3.8%
س 1
 
3.8%
Other values (6) 6
23.1%
Hangul
ValueCountFrequency (%)
3
 
4.5%
3
 
4.5%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (38) 43
65.2%
Latin Ext Additional
ValueCountFrequency (%)
3
23.1%
2
15.4%
2
15.4%
2
15.4%
2
15.4%
2
15.4%
Devanagari
ValueCountFrequency (%)
2
100.0%
Number Forms
ValueCountFrequency (%)
2
100.0%
VS
ValueCountFrequency (%)
2
100.0%
Dingbats
ValueCountFrequency (%)
2
100.0%
Arrows
ValueCountFrequency (%)
2
100.0%
Geometric Shapes
ValueCountFrequency (%)
1
50.0%
1
50.0%
Thai
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Misc Technical
ValueCountFrequency (%)
1
100.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%
PUA
ValueCountFrequency (%)
1
100.0%
Math Operators
ValueCountFrequency (%)
1
50.0%
1
50.0%
Arabic PF B
ValueCountFrequency (%)
 1
100.0%
Modifier Letters
ValueCountFrequency (%)
˙ 1
100.0%

popularity
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.238535
Minimum0
Maximum100
Zeros16020
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2023-11-10T14:11:29.132455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median35
Q350
95-th percentile69
Maximum100
Range100
Interquartile range (IQR)33

Descriptive statistics

Standard deviation22.305078
Coefficient of variation (CV)0.67106082
Kurtosis-0.92775532
Mean33.238535
Median Absolute Deviation (MAD)16
Skewness0.046402516
Sum3789193
Variance497.51653
MonotonicityNot monotonic
2023-11-10T14:11:29.317407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16020
 
14.1%
22 2354
 
2.1%
21 2344
 
2.1%
44 2288
 
2.0%
1 2140
 
1.9%
23 2117
 
1.9%
20 2110
 
1.9%
43 2073
 
1.8%
45 2004
 
1.8%
41 1996
 
1.8%
Other values (91) 78554
68.9%
ValueCountFrequency (%)
0 16020
14.1%
1 2140
 
1.9%
2 1036
 
0.9%
3 585
 
0.5%
4 389
 
0.3%
5 599
 
0.5%
6 426
 
0.4%
7 465
 
0.4%
8 544
 
0.5%
9 525
 
0.5%
ValueCountFrequency (%)
100 2
 
< 0.1%
99 1
 
< 0.1%
98 7
< 0.1%
97 8
< 0.1%
96 7
< 0.1%
95 5
< 0.1%
94 7
< 0.1%
93 12
< 0.1%
92 9
< 0.1%
91 10
< 0.1%

duration_ms
Real number (ℝ)

Distinct50697
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228029.15
Minimum0
Maximum5237295
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2023-11-10T14:11:29.514003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile116920
Q1174066
median212906
Q3261506
95-th percentile387167.1
Maximum5237295
Range5237295
Interquartile range (IQR)87440

Descriptive statistics

Standard deviation107297.71
Coefficient of variation (CV)0.47054384
Kurtosis354.95242
Mean228029.15
Median Absolute Deviation (MAD)42760
Skewness11.195181
Sum2.5995323 × 1010
Variance1.1512799 × 1010
MonotonicityNot monotonic
2023-11-10T14:11:29.710165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162897 146
 
0.1%
180000 104
 
0.1%
192000 91
 
0.1%
240000 84
 
0.1%
118840 76
 
0.1%
172342 75
 
0.1%
227520 71
 
0.1%
131733 70
 
0.1%
243057 66
 
0.1%
175986 63
 
0.1%
Other values (50687) 113154
99.3%
ValueCountFrequency (%)
0 1
< 0.1%
8586 1
< 0.1%
13386 1
< 0.1%
15800 1
< 0.1%
17453 1
< 0.1%
17826 2
< 0.1%
21120 1
< 0.1%
21240 1
< 0.1%
22266 1
< 0.1%
23506 2
< 0.1%
ValueCountFrequency (%)
5237295 1
< 0.1%
4789026 2
< 0.1%
4730302 1
< 0.1%
4563897 1
< 0.1%
4447520 1
< 0.1%
4339826 1
< 0.1%
4334721 1
< 0.1%
4246206 1
< 0.1%
4120258 1
< 0.1%
3876276 2
< 0.1%

explicit
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size111.5 KiB
False
104253 
True
 
9747
ValueCountFrequency (%)
False 104253
91.5%
True 9747
 
8.6%
2023-11-10T14:11:29.960896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

danceability
Real number (ℝ)

Distinct1174
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56680007
Minimum0
Maximum0.985
Zeros157
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2023-11-10T14:11:30.224747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.456
median0.58
Q30.695
95-th percentile0.824
Maximum0.985
Range0.985
Interquartile range (IQR)0.239

Descriptive statistics

Standard deviation0.17354217
Coefficient of variation (CV)0.30617882
Kurtosis-0.18450245
Mean0.56680007
Median Absolute Deviation (MAD)0.119
Skewness-0.39949663
Sum64615.208
Variance0.030116886
MonotonicityNot monotonic
2023-11-10T14:11:30.486610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.647 431
 
0.4%
0.609 357
 
0.3%
0.579 347
 
0.3%
0.685 335
 
0.3%
0.602 334
 
0.3%
0.524 317
 
0.3%
0.689 315
 
0.3%
0.598 312
 
0.3%
0.607 307
 
0.3%
0.626 306
 
0.3%
Other values (1164) 110639
97.1%
ValueCountFrequency (%)
0 157
0.1%
0.0513 1
 
< 0.1%
0.0532 1
 
< 0.1%
0.0545 1
 
< 0.1%
0.0548 1
 
< 0.1%
0.055 1
 
< 0.1%
0.0555 1
 
< 0.1%
0.0558 1
 
< 0.1%
0.0562 1
 
< 0.1%
0.0565 2
 
< 0.1%
ValueCountFrequency (%)
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 1
 
< 0.1%
0.982 1
 
< 0.1%
0.981 2
< 0.1%
0.98 2
< 0.1%
0.979 2
< 0.1%
0.978 3
< 0.1%
0.977 1
 
< 0.1%
0.976 4
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct2083
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64138276
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2023-11-10T14:11:30.753052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.154
Q10.472
median0.685
Q30.854
95-th percentile0.969
Maximum1
Range1
Interquartile range (IQR)0.382

Descriptive statistics

Standard deviation0.25152907
Coefficient of variation (CV)0.39216687
Kurtosis-0.52571082
Mean0.64138276
Median Absolute Deviation (MAD)0.186
Skewness-0.59700142
Sum73117.634
Variance0.063266872
MonotonicityNot monotonic
2023-11-10T14:11:30.991251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.876 318
 
0.3%
0.937 269
 
0.2%
0.931 261
 
0.2%
0.886 258
 
0.2%
0.801 258
 
0.2%
0.948 254
 
0.2%
0.858 254
 
0.2%
0.961 254
 
0.2%
0.92 240
 
0.2%
0.981 238
 
0.2%
Other values (2073) 111396
97.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1.95 × 10-51
 
< 0.1%
2.01 × 10-513
 
< 0.1%
2.02 × 10-54
 
< 0.1%
2.03 × 10-534
< 0.1%
2.82 × 10-51
 
< 0.1%
3.05 × 10-51
 
< 0.1%
3.61 × 10-51
 
< 0.1%
4.28 × 10-53
 
< 0.1%
5.9 × 10-52
 
< 0.1%
ValueCountFrequency (%)
1 28
 
< 0.1%
0.999 100
0.1%
0.998 149
0.1%
0.997 165
0.1%
0.996 159
0.1%
0.995 229
0.2%
0.994 173
0.2%
0.993 184
0.2%
0.992 161
0.1%
0.991 200
0.2%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3091404
Minimum0
Maximum11
Zeros13061
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2023-11-10T14:11:31.235668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5599871
Coefficient of variation (CV)0.67053928
Kurtosis-1.2765712
Mean5.3091404
Median Absolute Deviation (MAD)3
Skewness-0.0085003605
Sum605242
Variance12.673508
MonotonicityNot monotonic
2023-11-10T14:11:31.429440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 13245
11.6%
0 13061
11.5%
2 11644
10.2%
9 11313
9.9%
1 10772
9.4%
5 9368
8.2%
11 9282
8.1%
4 9008
7.9%
6 7921
6.9%
10 7456
6.5%
Other values (2) 10930
9.6%
ValueCountFrequency (%)
0 13061
11.5%
1 10772
9.4%
2 11644
10.2%
3 3570
 
3.1%
4 9008
7.9%
5 9368
8.2%
6 7921
6.9%
7 13245
11.6%
8 7360
6.5%
9 11313
9.9%
ValueCountFrequency (%)
11 9282
8.1%
10 7456
6.5%
9 11313
9.9%
8 7360
6.5%
7 13245
11.6%
6 7921
6.9%
5 9368
8.2%
4 9008
7.9%
3 3570
 
3.1%
2 11644
10.2%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct19480
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.2589604
Minimum-49.531
Maximum4.532
Zeros0
Zeros (%)0.0%
Negative113910
Negative (%)99.9%
Memory size890.8 KiB
2023-11-10T14:11:32.069966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-49.531
5-th percentile-18.067
Q1-10.013
median-7.004
Q3-5.003
95-th percentile-2.974
Maximum4.532
Range54.063
Interquartile range (IQR)5.01

Descriptive statistics

Standard deviation5.0293366
Coefficient of variation (CV)-0.60895517
Kurtosis5.8962782
Mean-8.2589604
Median Absolute Deviation (MAD)2.343
Skewness-2.0065419
Sum-941521.48
Variance25.294227
MonotonicityNot monotonic
2023-11-10T14:11:32.343884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.662 176
 
0.2%
-4.457 90
 
0.1%
-9.336 86
 
0.1%
-7.57 77
 
0.1%
-4.034 75
 
0.1%
-8.871 74
 
0.1%
-3.725 72
 
0.1%
-4.324 70
 
0.1%
-5.08 64
 
0.1%
-12.472 64
 
0.1%
Other values (19470) 113152
99.3%
ValueCountFrequency (%)
-49.531 1
 
< 0.1%
-49.307 1
 
< 0.1%
-46.591 1
 
< 0.1%
-46.251 1
 
< 0.1%
-43.957 1
 
< 0.1%
-43.943 1
 
< 0.1%
-43.714 1
 
< 0.1%
-43.504 1
 
< 0.1%
-43.303 1
 
< 0.1%
-43.046 3
< 0.1%
ValueCountFrequency (%)
4.532 1
< 0.1%
3.156 1
< 0.1%
2.574 1
< 0.1%
1.864 1
< 0.1%
1.821 1
< 0.1%
1.795 1
< 0.1%
1.7 1
< 0.1%
1.682 1
< 0.1%
1.673 1
< 0.1%
1.416 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size890.8 KiB
1
72681 
0
41319 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters114000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 72681
63.8%
0 41319
36.2%

Length

2023-11-10T14:11:32.555637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T14:11:32.743094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 72681
63.8%
0 41319
36.2%

Most occurring characters

ValueCountFrequency (%)
1 72681
63.8%
0 41319
36.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 114000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 72681
63.8%
0 41319
36.2%

Most occurring scripts

ValueCountFrequency (%)
Common 114000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 72681
63.8%
0 41319
36.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 72681
63.8%
0 41319
36.2%

speechiness
Real number (ℝ)

Distinct1489
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.084652112
Minimum0
Maximum0.965
Zeros157
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2023-11-10T14:11:32.952716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0282
Q10.0359
median0.0489
Q30.0845
95-th percentile0.268
Maximum0.965
Range0.965
Interquartile range (IQR)0.0486

Descriptive statistics

Standard deviation0.10573236
Coefficient of variation (CV)1.2490222
Kurtosis28.824377
Mean0.084652112
Median Absolute Deviation (MAD)0.0165
Skewness4.647516
Sum9650.3408
Variance0.011179333
MonotonicityNot monotonic
2023-11-10T14:11:33.184989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0323 400
 
0.4%
0.0324 376
 
0.3%
0.0322 373
 
0.3%
0.0328 363
 
0.3%
0.0295 358
 
0.3%
0.0321 352
 
0.3%
0.033 347
 
0.3%
0.0367 346
 
0.3%
0.0326 340
 
0.3%
0.0306 332
 
0.3%
Other values (1479) 110413
96.9%
ValueCountFrequency (%)
0 157
0.1%
0.0221 3
 
< 0.1%
0.0222 1
 
< 0.1%
0.0223 3
 
< 0.1%
0.0225 2
 
< 0.1%
0.0226 2
 
< 0.1%
0.0227 3
 
< 0.1%
0.0228 5
 
< 0.1%
0.0229 1
 
< 0.1%
0.023 9
 
< 0.1%
ValueCountFrequency (%)
0.965 1
 
< 0.1%
0.963 2
 
< 0.1%
0.962 6
< 0.1%
0.961 2
 
< 0.1%
0.96 3
 
< 0.1%
0.959 6
< 0.1%
0.958 6
< 0.1%
0.957 8
< 0.1%
0.956 7
< 0.1%
0.955 11
< 0.1%

acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct5061
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31491006
Minimum0
Maximum0.996
Zeros39
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2023-11-10T14:11:33.400101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000145
Q10.0169
median0.169
Q30.598
95-th percentile0.948
Maximum0.996
Range0.996
Interquartile range (IQR)0.5811

Descriptive statistics

Standard deviation0.3325227
Coefficient of variation (CV)1.0559291
Kurtosis-0.94993129
Mean0.31491006
Median Absolute Deviation (MAD)0.1675
Skewness0.72729486
Sum35899.747
Variance0.11057135
MonotonicityNot monotonic
2023-11-10T14:11:33.599880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 305
 
0.3%
0.993 267
 
0.2%
0.994 266
 
0.2%
0.992 250
 
0.2%
0.991 218
 
0.2%
0.131 206
 
0.2%
0.881 204
 
0.2%
0.108 195
 
0.2%
0.107 190
 
0.2%
0.99 189
 
0.2%
Other values (5051) 111710
98.0%
ValueCountFrequency (%)
0 39
< 0.1%
1 × 10-61
 
< 0.1%
1.01 × 10-64
 
< 0.1%
1.02 × 10-61
 
< 0.1%
1.03 × 10-62
 
< 0.1%
1.04 × 10-64
 
< 0.1%
1.06 × 10-65
 
< 0.1%
1.07 × 10-64
 
< 0.1%
1.08 × 10-62
 
< 0.1%
1.09 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.996 103
 
0.1%
0.995 305
0.3%
0.994 266
0.2%
0.993 267
0.2%
0.992 250
0.2%
0.991 218
0.2%
0.99 189
0.2%
0.989 177
0.2%
0.988 150
0.1%
0.987 158
0.1%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct5346
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15604959
Minimum0
Maximum1
Zeros38763
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2023-11-10T14:11:33.867454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.16 × 10-5
Q30.049
95-th percentile0.904
Maximum1
Range1
Interquartile range (IQR)0.049

Descriptive statistics

Standard deviation0.30955485
Coefficient of variation (CV)1.9836954
Kurtosis1.2707471
Mean0.15604959
Median Absolute Deviation (MAD)4.16 × 10-5
Skewness1.7344062
Sum17789.653
Variance0.095824204
MonotonicityNot monotonic
2023-11-10T14:11:34.130741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38763
34.0%
3.59 × 10-5166
 
0.1%
0.895 122
 
0.1%
0.905 122
 
0.1%
0.934 121
 
0.1%
0.922 118
 
0.1%
0.911 115
 
0.1%
0.000141 115
 
0.1%
0.913 114
 
0.1%
0.9 114
 
0.1%
Other values (5336) 74130
65.0%
ValueCountFrequency (%)
0 38763
34.0%
1 × 10-632
 
< 0.1%
1.01 × 10-646
 
< 0.1%
1.02 × 10-636
 
< 0.1%
1.03 × 10-634
 
< 0.1%
1.04 × 10-650
 
< 0.1%
1.05 × 10-639
 
< 0.1%
1.06 × 10-649
 
< 0.1%
1.07 × 10-656
 
< 0.1%
1.08 × 10-647
 
< 0.1%
ValueCountFrequency (%)
1 13
< 0.1%
0.999 22
< 0.1%
0.998 6
 
< 0.1%
0.997 11
< 0.1%
0.996 4
 
< 0.1%
0.995 15
< 0.1%
0.994 4
 
< 0.1%
0.993 9
< 0.1%
0.992 11
< 0.1%
0.991 12
< 0.1%

liveness
Real number (ℝ)

Distinct1722
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21355284
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2023-11-10T14:11:34.400694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0606
Q10.098
median0.132
Q30.273
95-th percentile0.681
Maximum1
Range1
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.1903777
Coefficient of variation (CV)0.89147821
Kurtosis4.3782683
Mean0.21355284
Median Absolute Deviation (MAD)0.051
Skewness2.1057381
Sum24345.023
Variance0.036243668
MonotonicityNot monotonic
2023-11-10T14:11:34.634521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.108 1353
 
1.2%
0.111 1318
 
1.2%
0.109 1198
 
1.1%
0.11 1179
 
1.0%
0.105 1114
 
1.0%
0.107 1102
 
1.0%
0.103 1094
 
1.0%
0.106 1064
 
0.9%
0.112 1063
 
0.9%
0.113 1008
 
0.9%
Other values (1712) 102507
89.9%
ValueCountFrequency (%)
0 2
< 0.1%
0.00925 1
< 0.1%
0.00986 1
< 0.1%
0.0112 1
< 0.1%
0.0114 1
< 0.1%
0.0116 1
< 0.1%
0.0118 1
< 0.1%
0.0133 1
< 0.1%
0.0136 1
< 0.1%
0.0137 1
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.997 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 3
 
< 0.1%
0.993 2
 
< 0.1%
0.992 9
< 0.1%
0.991 4
 
< 0.1%
0.99 11
< 0.1%
0.989 17
< 0.1%
0.988 17
< 0.1%

valence
Real number (ℝ)

Distinct1790
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47406823
Minimum0
Maximum0.995
Zeros176
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2023-11-10T14:11:34.936537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0708
Q10.26
median0.464
Q30.683
95-th percentile0.911
Maximum0.995
Range0.995
Interquartile range (IQR)0.423

Descriptive statistics

Standard deviation0.25926106
Coefficient of variation (CV)0.54688555
Kurtosis-1.0274297
Mean0.47406823
Median Absolute Deviation (MAD)0.212
Skewness0.11507804
Sum54043.778
Variance0.0672163
MonotonicityNot monotonic
2023-11-10T14:11:35.168756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 300
 
0.3%
0.304 248
 
0.2%
0.717 233
 
0.2%
0.962 230
 
0.2%
0.324 225
 
0.2%
0.963 216
 
0.2%
0.55 210
 
0.2%
0.365 205
 
0.2%
0.949 204
 
0.2%
0.202 201
 
0.2%
Other values (1780) 111728
98.0%
ValueCountFrequency (%)
0 176
0.2%
1 × 10-5129
0.1%
0.000322 1
 
< 0.1%
0.000378 1
 
< 0.1%
0.000667 1
 
< 0.1%
0.000673 1
 
< 0.1%
0.000755 1
 
< 0.1%
0.000781 1
 
< 0.1%
0.00084 1
 
< 0.1%
0.000885 1
 
< 0.1%
ValueCountFrequency (%)
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.993 3
< 0.1%
0.992 4
< 0.1%
0.991 3
< 0.1%
0.99 1
 
< 0.1%
0.989 1
 
< 0.1%
0.988 4
< 0.1%
0.987 2
< 0.1%
0.986 1
 
< 0.1%

tempo
Real number (ℝ)

Distinct45653
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.14784
Minimum0
Maximum243.372
Zeros157
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2023-11-10T14:11:35.386067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.3469
Q199.21875
median122.017
Q3140.071
95-th percentile175.06715
Maximum243.372
Range243.372
Interquartile range (IQR)40.85225

Descriptive statistics

Standard deviation29.978197
Coefficient of variation (CV)0.24542552
Kurtosis-0.10858061
Mean122.14784
Median Absolute Deviation (MAD)21.7025
Skewness0.23229486
Sum13924853
Variance898.69229
MonotonicityNot monotonic
2023-11-10T14:11:35.616605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 157
 
0.1%
151.925 146
 
0.1%
95.004 95
 
0.1%
87.925 76
 
0.1%
130.594 76
 
0.1%
92.988 70
 
0.1%
125.004 70
 
0.1%
76.783 69
 
0.1%
77.321 67
 
0.1%
90.04 63
 
0.1%
Other values (45643) 113111
99.2%
ValueCountFrequency (%)
0 157
0.1%
30.2 1
 
< 0.1%
30.322 1
 
< 0.1%
31.834 1
 
< 0.1%
34.262 1
 
< 0.1%
34.821 1
 
< 0.1%
35.392 1
 
< 0.1%
35.79 1
 
< 0.1%
35.862 1
 
< 0.1%
35.928 1
 
< 0.1%
ValueCountFrequency (%)
243.372 1
 
< 0.1%
222.605 1
 
< 0.1%
220.525 1
 
< 0.1%
220.084 1
 
< 0.1%
220.081 3
< 0.1%
220.039 1
 
< 0.1%
219.971 1
 
< 0.1%
219.693 1
 
< 0.1%
219.571 1
 
< 0.1%
218.879 1
 
< 0.1%

time_signature
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size890.8 KiB
4
101843 
3
 
9195
5
 
1826
1
 
973
0
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters114000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 101843
89.3%
3 9195
 
8.1%
5 1826
 
1.6%
1 973
 
0.9%
0 163
 
0.1%

Length

2023-11-10T14:11:35.786555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T14:11:35.983346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 101843
89.3%
3 9195
 
8.1%
5 1826
 
1.6%
1 973
 
0.9%
0 163
 
0.1%

Most occurring characters

ValueCountFrequency (%)
4 101843
89.3%
3 9195
 
8.1%
5 1826
 
1.6%
1 973
 
0.9%
0 163
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 114000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 101843
89.3%
3 9195
 
8.1%
5 1826
 
1.6%
1 973
 
0.9%
0 163
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 114000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 101843
89.3%
3 9195
 
8.1%
5 1826
 
1.6%
1 973
 
0.9%
0 163
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 101843
89.3%
3 9195
 
8.1%
5 1826
 
1.6%
1 973
 
0.9%
0 163
 
0.1%
Distinct114
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size890.8 KiB
2023-11-10T14:11:36.364657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length17
Median length11
Mean length7.0701754
Min length3

Characters and Unicode

Total characters806000
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowacoustic
2nd rowacoustic
3rd rowacoustic
4th rowacoustic
5th rowacoustic
ValueCountFrequency (%)
acoustic 1000
 
0.9%
drum-and-bass 1000
 
0.9%
alternative 1000
 
0.9%
ambient 1000
 
0.9%
anime 1000
 
0.9%
black-metal 1000
 
0.9%
bluegrass 1000
 
0.9%
blues 1000
 
0.9%
brazil 1000
 
0.9%
breakbeat 1000
 
0.9%
Other values (104) 104000
91.2%
2023-11-10T14:11:37.591811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 73000
 
9.1%
a 68000
 
8.4%
o 67000
 
8.3%
r 57000
 
7.1%
n 50000
 
6.2%
i 47000
 
5.8%
s 44000
 
5.5%
t 43000
 
5.3%
p 39000
 
4.8%
l 39000
 
4.8%
Other values (15) 279000
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 772000
95.8%
Dash Punctuation 34000
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 73000
 
9.5%
a 68000
 
8.8%
o 67000
 
8.7%
r 57000
 
7.4%
n 50000
 
6.5%
i 47000
 
6.1%
s 44000
 
5.7%
t 43000
 
5.6%
p 39000
 
5.1%
l 39000
 
5.1%
Other values (14) 245000
31.7%
Dash Punctuation
ValueCountFrequency (%)
- 34000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 772000
95.8%
Common 34000
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 73000
 
9.5%
a 68000
 
8.8%
o 67000
 
8.7%
r 57000
 
7.4%
n 50000
 
6.5%
i 47000
 
6.1%
s 44000
 
5.7%
t 43000
 
5.6%
p 39000
 
5.1%
l 39000
 
5.1%
Other values (14) 245000
31.7%
Common
ValueCountFrequency (%)
- 34000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 806000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 73000
 
9.1%
a 68000
 
8.4%
o 67000
 
8.3%
r 57000
 
7.1%
n 50000
 
6.2%
i 47000
 
5.8%
s 44000
 
5.5%
t 43000
 
5.3%
p 39000
 
4.8%
l 39000
 
4.8%
Other values (15) 279000
34.6%

Interactions

2023-11-10T14:11:18.724089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:49.023331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:51.549715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:54.187456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:56.462299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:58.658011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:01.304283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:03.522463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:05.740249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:08.737304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:11.471090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:13.738364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:16.309347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:18.875116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:49.241711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:51.839556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:54.440818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:56.614286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:58.856315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:01.475346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:03.670763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:05.893441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:08.927342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:11.631093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:13.888553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:16.503025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:19.262375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:49.391417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:52.102542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:54.612552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:56.757537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:59.054877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:01.639270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:04.039439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:06.051757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:09.112685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:11.792590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:14.068031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:16.711644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:19.411449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:49.558338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:52.368784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:54.779395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:56.929549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:59.263252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:01.802778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:04.207331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:06.632879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:09.283423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:12.030313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:14.315922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:16.898994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:19.564025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:49.708253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:52.535405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:55.022774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:57.086026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:59.454337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:02.019260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:04.363405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:06.913895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:09.567535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:12.214386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:14.526952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:17.081272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:19.720024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:49.857202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:52.684539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:55.188224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:57.239608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:59.657253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:02.269904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:04.518082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:07.156270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:09.722564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:12.355838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:14.716903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:17.285187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:19.854305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:50.012179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:52.863098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:55.323860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:57.387594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:59.830246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:02.464593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:04.656319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:07.414081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:09.887988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:12.520966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:14.924823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:17.454689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:20.020015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:50.157488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:53.088515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:55.489942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:57.557236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:00.023729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:02.613585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:04.815983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:07.603622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:10.047105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:12.682190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:15.147173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:17.616571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:20.174169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:50.331147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:53.257744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:55.644356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:57.722907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:00.204600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:02.754281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:04.975627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:07.765831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:10.260944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:12.906579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:15.334464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:17.800164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:20.328033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:50.482794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:53.412776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:55.790703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:57.874846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:00.430397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:02.904558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:05.122905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:08.020709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:10.631565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:13.088520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:15.523869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:18.018953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:20.479906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:50.624580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:53.563692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:55.938013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:58.098724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:00.608570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:03.056651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:05.280003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:08.155608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:10.953700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:13.272591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:15.707803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:18.218562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:20.659131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:50.799340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:53.715641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:56.132021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:58.302318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:00.808054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:03.223060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:05.433555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:08.332383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:11.138724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:13.440460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:15.920140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:18.386920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:20.839494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:50.941263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:53.923640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:56.311541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:10:58.478681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:01.101668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:03.375072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:05.592412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:08.523729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:11.312018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:13.599348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:16.129413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-10T14:11:18.574117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-10T14:11:37.822801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Unnamed: 0popularityduration_msdanceabilityenergykeyloudnessspeechinessacousticnessinstrumentalnesslivenessvalencetempoexplicitmodetime_signature
Unnamed: 01.0000.033-0.0290.007-0.064-0.006-0.043-0.0420.100-0.0840.0270.053-0.0250.1020.0720.070
popularity0.0331.0000.0280.027-0.024-0.0030.035-0.0680.008-0.078-0.008-0.0420.0170.0890.0370.046
duration_ms-0.0290.0281.000-0.0980.1040.0140.022-0.129-0.1700.127-0.040-0.1780.0500.0110.0040.036
danceability0.0070.027-0.0981.0000.0390.0350.1120.159-0.039-0.144-0.1450.462-0.0710.1540.0850.279
energy-0.064-0.0240.1040.0391.0000.0450.7500.355-0.708-0.0350.1770.2080.2410.1160.0870.161
key-0.006-0.0030.0140.0350.0451.0000.0320.044-0.0380.005-0.0040.0330.0120.0400.2470.021
loudness-0.0430.0350.0220.1120.7500.0321.0000.232-0.534-0.2890.1110.2210.1940.1080.0450.152
speechiness-0.042-0.068-0.1290.1590.3550.0440.2321.000-0.214-0.0490.0920.0920.1150.3060.0670.085
acousticness0.1000.008-0.170-0.039-0.708-0.038-0.534-0.2141.000-0.096-0.042-0.021-0.2170.1020.1000.141
instrumentalness-0.084-0.0780.127-0.144-0.0350.005-0.289-0.049-0.0961.000-0.099-0.320-0.0050.1040.0590.067
liveness0.027-0.008-0.040-0.1450.177-0.0040.1110.092-0.042-0.0991.0000.0130.0190.0420.0290.040
valence0.053-0.042-0.1780.4620.2080.0330.2210.092-0.021-0.3200.0131.0000.0630.0690.0330.111
tempo-0.0250.0170.050-0.0710.2410.0120.1940.115-0.217-0.0050.0190.0631.0000.0400.0260.496
explicit0.1020.0890.0110.1540.1160.0400.1080.3060.1020.1040.0420.0690.0401.0000.0370.060
mode0.0720.0370.0040.0850.0870.2470.0450.0670.1000.0590.0290.0330.0260.0371.0000.028
time_signature0.0700.0460.0360.2790.1610.0210.1520.0850.1410.0670.0400.1110.4960.0600.0281.000

Missing values

2023-11-10T14:11:21.623549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-10T14:11:22.556346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-10T14:11:23.265456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0track_idartistsalbum_nametrack_namepopularityduration_msexplicitdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signaturetrack_genre
005SuOikwiRyPMVoIQDJUgSVGen HoshinoComedyComedy73230666False0.6760.46101-6.74600.14300.03220.0000010.35800.715087.9174acoustic
114qPNDBW1i3p13qLCt0Ki3ABen WoodwardGhost (Acoustic)Ghost - Acoustic55149610False0.4200.16601-17.23510.07630.92400.0000060.10100.267077.4894acoustic
221iJBSr7s7jYXzM8EGcbK5bIngrid Michaelson;ZAYNTo Begin AgainTo Begin Again57210826False0.4380.35900-9.73410.05570.21000.0000000.11700.120076.3324acoustic
336lfxq3CG4xtTiEg7opyCyxKina GrannisCrazy Rich Asians (Original Motion Picture Soundtrack)Can't Help Falling In Love71201933False0.2660.05960-18.51510.03630.90500.0000710.13200.1430181.7403acoustic
445vjLSffimiIP26QG5WcN2KChord OverstreetHold OnHold On82198853False0.6180.44302-9.68110.05260.46900.0000000.08290.1670119.9494acoustic
5501MVOl9KtVTNfFiBU9I7dcTyrone WellsDays I Will RememberDays I Will Remember58214240False0.6880.48106-8.80710.10500.28900.0000000.18900.666098.0174acoustic
666Vc5wAMmXdKIAM7WUoEb7NA Great Big World;Christina AguileraIs There Anybody Out There?Say Something74229400False0.4070.14702-8.82210.03550.85700.0000030.09130.0765141.2843acoustic
771EzrEOXmMH3G43AXT1y7pAJason MrazWe Sing. We Dance. We Steal Things.I'm Yours80242946False0.7030.444011-9.33110.04170.55900.0000000.09730.7120150.9604acoustic
880IktbUcnAGrvD03AWnz3Q8Jason Mraz;Colbie CaillatWe Sing. We Dance. We Steal Things.Lucky74189613False0.6250.41400-8.70010.03690.29400.0000000.15100.6690130.0884acoustic
997k9GuJYLp2AzqokyEdwEw2Ross CoppermanHungerHunger56205594False0.4420.63201-6.77010.02950.42600.0041900.07350.196078.8994acoustic
Unnamed: 0track_idartistsalbum_nametrack_namepopularityduration_msexplicitdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signaturetrack_genre
1139901139902A4dSiJmbviL56CBupkh6CLucas CervettiFrecuencias Álmicas en 432hz (Solo Piano)Frecuencia Álmica XI - Solo Piano22369049False0.5790.2454-16.35710.03840.970000.9240000.10100.3020112.0113world-music
1139911139910CE0Y6GM75cbrqao8EOAlWChris TomlinThe Ultimate PlaylistAt The Cross (Love Ran Red)32250629False0.3870.5318-4.78810.02900.003050.0000000.20100.1530146.0034world-music
1139921139923FjOBB4EyIXHYUtSgrIdY9Jesus CultureRevelation SongsYour Love Never Fails38312566False0.4750.86010-4.72210.04210.006500.0000020.24600.4270113.9494world-music
1139931139934OkMK49i3NApR1KsAIsTf6Chris TomlinSee The Morning (Special Edition)How Can I Keep From Singing39256026False0.5050.68710-4.37510.02870.084100.0000000.18800.3820104.0833world-music
1139941139944WbOUe6T0sozC7z5ZJgiAALucas CervettiFrecuencias Álmicas en 432hzFrecuencia Álmica, Pt. 422305454False0.3310.1711-15.66810.03500.920000.0229000.06790.3270132.1473world-music
1139951139952C3TZjDRiAzdyViavDJ217Rainy Lullaby#mindfulness - Soft Rain for Mindful Meditation, Stress Relief Relaxation MusicSleep My Little Boy21384999False0.1720.2355-16.39310.04220.640000.9280000.08630.0339125.9955world-music
1139961139961hIz5L4IB9hN3WRYPOCGPwRainy Lullaby#mindfulness - Soft Rain for Mindful Meditation, Stress Relief Relaxation MusicWater Into Light22385000False0.1740.1170-18.31800.04010.994000.9760000.10500.035085.2394world-music
1139971139976x8ZfSoqDjuNa5SVP5QjvXCesária EvoraBest OfMiss Perfumado22271466False0.6290.3290-10.89500.04200.867000.0000000.08390.7430132.3784world-music
1139981139982e6sXL2bYv4bSz6VTdnfLsMichael W. SmithChange Your WorldFriends41283893False0.5870.5067-10.88910.02970.381000.0000000.27000.4130135.9604world-music
1139991139992hETkH7cOfqmz3LqZDHZf5Cesária EvoraMiss PerfumadoBarbincor22241826False0.5260.4871-10.20400.07250.681000.0000000.08930.708079.1984world-music